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Trained using only image class label, deep weakly supervised methods allowimage classification and ROI segmentation for interpretability. Despite theirsuccess on natural images, they face several challenges over histology datawhere ROI are visually similar to background making models vulnerable to highpixel-wise false positives. These methods lack mechanisms for modelingexplicitly non-discriminative regions which raises false-positive rates. Wepropose novel regularization terms, which enable the model to seek bothnon-discriminative and discriminative regions, while discouraging unbalancedsegmentations and using only image class label. Our method is composed of twonetworks: a localizer that yields segmentation mask, followed by a classifier.The training loss pushes the localizer to build a segmentation mask that holdsmost discrimiantive regions while simultaneously modeling background regions.Comprehensive experiments over two histology datasets showed the merits of ourmethod in reducing false positives and accurately segmenting ROI.

Leveraging Uncertainty for Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images